PSCI 2270 - Lecture 2
Department of Political Science, Vanderbilt University
September 5, 2023
Causal Theories
From Theory to Hypothesis
Operationalization of Theory
From Population to Sample
Predictive: Forecast future events from data
Descriptive: Summarize data, investigate facts, discover hidden patterns
Causal: Answer what-if’s
Correlation: is any statistical association, though it commonly refers to the degree to which a pair of variables are linearly related.
Causation: indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events. This is also referred to as cause and effect.
Confounder: (also confounding variable, omitted variable, or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association.
Suppose there are two factors that we know are positively correlated with each other (e.g. when \(X\) is higher, \(Y\) tends to be higher too).
\(X\) usually refers to independent/explanatory variable; \(Y\) – to dependent/outcome variable
Claim: Internet searches for the word “flu” increase the incidence of flu in the city from which the search arose.
Evidence: The more people in a city who do a Google search for the word “flu”, the more cases of flu there tend to be in that city.
Claim: Smoking causes lung cancer.
Evidence: People who smoke are more likely to contract lung cancer than people who don’t smoke.
Claim: Cell phone access increases violent protest.
Evidence: When a region in Africa gets cell phone coverage the frequency of violent political protests in the next year goes up.
Claim: Experience of civil war causes people to develop more violent personalities.
Evidence: The more years of civil war a country has experienced since 1945, the more yellow and red cards its nationals get in club and international soccer matches.
Claim: Giving a student high grades causes them to perform better on standardized tests.
Evidence: Teenagers with higher high school GPAs get better scores on their SATs.
Claim: Super Bowl appearances are bad for health in the team’s home town.
Evidence: If you live in a town whose team makes it to the Super Bowl you are more likely to die from the flu in that year.
A DAG displays assumptions about the relationship between variables (nodes).
Do NOT do THIS! 😵
Concepts are latent:
Indicators are concrete:
Sometimes there is slippage between latent concept and proxy, e.g.
Important to make measurement as unobtrusive as possible
Validity: measure what you intend to measure
Reliability: same answer over repeated measurements
Measures of equality:
Combine this information:
At the individual level:
At the country level: